{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备数据:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import cifar10\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one-hot前的形状\n",
      "(50000, 1)\n",
      "(10000, 1)\n",
      "one-hot后的形状\n",
      "(50000, 10)\n",
      "(10000, 10)\n",
      "处理前的最大值为255.000000\n",
      "处理后的最大值1.000000\n",
      "x_train.shape: (50000, 32, 32, 3)\n",
      "x_test.shape: (10000, 32, 32, 3)\n",
      "32 32\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "\n",
    "(x_train_0, y_train_0), (x_test_0, y_test_0)=cifar10.load_data()\n",
    "print(\"one-hot前的形状\")\n",
    "print(y_train_0.shape)\n",
    "print(y_test_0.shape)\n",
    "y_train=np_utils.to_categorical(y_train_0)\n",
    "y_test=np_utils.to_categorical(y_test_0)\n",
    "print(\"one-hot后的形状\")\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "print(\"处理前的最大值为%f\" % x_train_0.max())\n",
    "x_train=x_train_0/255\n",
    "x_test=x_test_0/255\n",
    "print(\"处理后的最大值%f\" % x_train.max())\n",
    "print(\"x_train.shape:\", x_train.shape)\n",
    "print(\"x_test.shape:\", x_test.shape)\n",
    "\n",
    "width,height=x_train_0.shape[1], x_train_0.shape[2]\n",
    "print(width,height)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 尝试变换图像: (图像数据增强)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=30,      #随机旋转不超过30度\n",
    "    horizontal_flip=True,   #随机水平翻转\n",
    "    vertical_flip=True,     #随机竖直翻转\n",
    "    width_shift_range=5,    #随机水平平移不超过5像素\n",
    "    height_shift_range=5    #随机竖直平移不超过5像素\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#选取一张图像进行5次随机变换\n",
    "origin_image=x_train[6]\n",
    "\n",
    "\n",
    "\n",
    "#对图像做五次随机变换并画出来\n",
    "fig, ax = plt.subplots(2,5, figsize=(15,3))\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(5):\n",
    "        ax[i][j].set_xticks([])\n",
    "        ax[i][j].set_yticks([])\n",
    "\n",
    "for i in range(5):\n",
    "    ax[0][i].imshow(origin_image)\n",
    "    ax[1][i].imshow(datagen.random_transform(origin_image))   #使用datagen对图像做随机变换\n",
    "\n",
    "plt.tight_layout() #采用更紧凑美观的布局方式\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 构建模型:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 32\n",
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_8 (Conv2D)            (None, 28, 28, 32)        2432      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 12, 12, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 6, 6, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 1152)              0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 256)               295168    \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 309,418\n",
      "Trainable params: 309,418\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential      #线性模型框架\n",
    "from keras.layers import Dense, Dropout, Flatten   #Dense全连接层; Flatten平铺层\n",
    "from keras.layers import Conv2D, MaxPooling2D   #\n",
    "\n",
    "print(width, height)\n",
    "#keras中的线性模型\n",
    "model=Sequential()  \n",
    "#二维卷积层, 32个5X5的卷积核, 使用ReLU作为激活函数\n",
    "model.add(Conv2D(32,(5,5),activation=\"relu\",input_shape=(width,height,3))) \n",
    "#最大池化层: 2X2大小的池化核\n",
    "model.add(MaxPooling2D(pool_size=(2,2))) \n",
    "#二维卷积层: 32个3x3的卷积核,使用ReLU作为激活函数\n",
    "model.add(Conv2D(32, (3,3), activation=\"relu\"))  \n",
    "#最大池化层: 2X2大小的池化核\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))  \n",
    "#平铺层: 将数据形状转为向量\n",
    "model.add(Flatten())\n",
    "#全连接层: 隐藏层维度为256, 使用ReLU作为激活函数\n",
    "model.add(Dense(256, activation=\"relu\"))\n",
    "#全连接层: 隐藏层维度为10, 使用Softmax作为激活函数, 输出每个分类的概率\n",
    "model.add(Dense(10,activation=\"softmax\"))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "#优化器\n",
    "#Adam优化器, 损失函数:分类交叉熵\n",
    "#度量指标: 准确率\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 3. 开始训练:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre fit:1634196721.541947\n",
      "Epoch 1/10\n",
      "2084/2084 [==============================] - 41s 19ms/step - loss: 0.6114 - accuracy: 0.7846 - val_loss: 1.0325 - val_accuracy: 0.6685\n",
      "Epoch 2/10\n",
      "2084/2084 [==============================] - 42s 20ms/step - loss: 0.5238 - accuracy: 0.8155 - val_loss: 1.1200 - val_accuracy: 0.6584\n",
      "Epoch 3/10\n",
      "2084/2084 [==============================] - 42s 20ms/step - loss: 0.4415 - accuracy: 0.8444 - val_loss: 1.1319 - val_accuracy: 0.6716\n",
      "Epoch 4/10\n",
      "2084/2084 [==============================] - 40s 19ms/step - loss: 0.3690 - accuracy: 0.8681 - val_loss: 1.2288 - val_accuracy: 0.6643\n",
      "Epoch 5/10\n",
      "2084/2084 [==============================] - 42s 20ms/step - loss: 0.3094 - accuracy: 0.8902 - val_loss: 1.3493 - val_accuracy: 0.6654\n",
      "Epoch 6/10\n",
      "2084/2084 [==============================] - 41s 20ms/step - loss: 0.2594 - accuracy: 0.9080 - val_loss: 1.4565 - val_accuracy: 0.6683\n",
      "Epoch 7/10\n",
      "2084/2084 [==============================] - 41s 20ms/step - loss: 0.2146 - accuracy: 0.9251 - val_loss: 1.6705 - val_accuracy: 0.6665\n",
      "Epoch 8/10\n",
      "2084/2084 [==============================] - 42s 20ms/step - loss: 0.1944 - accuracy: 0.9310 - val_loss: 1.7503 - val_accuracy: 0.6647\n",
      "Epoch 9/10\n",
      "2084/2084 [==============================] - 41s 20ms/step - loss: 0.1682 - accuracy: 0.9415 - val_loss: 1.9478 - val_accuracy: 0.6537\n",
      "Epoch 10/10\n",
      "2084/2084 [==============================] - 38s 18ms/step - loss: 0.1513 - accuracy: 0.9469 - val_loss: 2.0084 - val_accuracy: 0.6619\n",
      "fit done:1634197132.089488\n",
      "duration:410.54754114151\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "start_time=time.time()\n",
    "print(\"pre fit:\" + str(start_time))\n",
    "# time.sleep(2)\n",
    "# model.fit(x_train, y_train, batch_size=24, epochs=10, validation_data=(x_test, y_test))\n",
    "model.fit_generator(datagen.flow(x_train, y_train, batch_size=24), epochs=10, validation_data=(x_test, y_test))\n",
    "end_time=time.time()\n",
    "print(\"fit done:\"+str(end_time))\n",
    "print(\"duration:\" + str(end_time-start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 结果展示:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 7ms/step - loss: 2.0084 - accuracy: 0.6619\n",
      "损失为2.008427\n",
      "准确度为0.661900\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test)\n",
    "print(\"损失为%f\" % score[0])\n",
    "print(\"准确度为%f\" % score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n",
      "[2419 1210 3427 9142  604 8263 3019 7547 4010 2722]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "labels = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']\n",
    "print(labels[0])\n",
    "error_id=np.random.choice(10000, size=10)\n",
    "print(error_id)\n",
    "\n",
    "y_predict=model.predict(x_test[error_id])\n",
    "# print(y_predict.shape)\n",
    "# for i in range(10):\n",
    "#     print(\"识别结果:%d\" % np.argmax(y_predict[i]))\n",
    "\n",
    "\n",
    "fig, axes=plt.subplots(2,5, figsize=(10,4))\n",
    "# print(axes.shape)\n",
    "axes=axes.flatten()\n",
    "# print(axes.shape)\n",
    "for i, idx in enumerate(error_id):\n",
    "    # print(i,idx)\n",
    "    axes[i].imshow(x_test[idx])\n",
    "    axes[i].set_xticks([])\n",
    "    axes[i].set_yticks([])\n",
    "    axes[i].set_title(\"y_predict: %s\\ny_test:%s\" % (labels[np.argmax(y_predict[i])], labels[np.argmax(y_test[idx])]))\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 图像数据的转换:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "tags": []
   },
   "source": [
    "# import glob\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "input_path=\"test/3.jpg\"\n",
    "output_path=\"test/3_out_sm.jpg\"\n",
    "im=Image.open(input_path)\n",
    "# im.show()\n",
    "\n",
    "im.thumbnail((28,28))\n",
    "print(im.format, im.size, im.mode)\n",
    "im.save(output_path)\n",
    "# im.show()\n",
    "\n",
    "im_1=im.convert(\"L\")\n",
    "print(\"im_1\",im_1.format, im_1.size, im_1.mode) \n",
    "im_1.save(\"test/3_out_gray.bmp\")\n",
    "im_data=im_1.getdata()\n",
    "im_array = np.array(list(im_data))\n",
    "im_array= im_array.reshape(28,28)\n",
    "im_array=255-im_array\n",
    "im_array=im_array.reshape(1,28,28,1)\n",
    "# print(im_array)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import matplotlib\n",
    "im = matplotlib.image.imread('test/3_out_gray.bmp')\n",
    "print (im.shape)\n",
    "# print(im)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
